Online Master’s in Data Analytics Courses

Maryville University Online Master’s in Data Analytics Curriculum

The online Master of Science in Data Analytics at Maryville University aims to prepare students for professional success in the field of data analytics.

You can complete your Maryville University online Master of Science in Data Analytics in just 30 credit hours. Start at the right time for you by choosing from six admission points throughout the year. Explore the curriculum below to see how you can complete your master’s in data analytics in as little as one year of full-time or two years of part-time study.

Foundational Courses

Admission Prerequisite: Depending on your background, a foundational course in statistics may be required. BUS-501, Survey of Business, will be required if your GPA is below a 3.0 and/or if your undergraduate degree was outside the area of business; however, credits earned in foundational courses (such as BUS 241 and BUS 501) are considered prerequisites to courses required for the graduate degree.

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BDAT 600	Data Analytics 1	3 Credits
This course serves as an introduction to data analytics. Data analytics is generally defined as the use of data, quantitative analysis, and modeling to drive business decisions. An overview of different analytics techniques (descriptive, predictive and prescriptive) with a focus on descriptive analytics technique to illustrate business decision-making situations is discussed. Cross-listed: ACCT-660, ISYS-660
BDAT 605	Database Principles	3 Credits
This course examines the design, development and management of relational databases and the integration of these databases with applications across the enterprise. Topics include database tools, basic Structured Query Language, design trade-offs, middleware, database server architectures, web-enabled technologies, security issues and emerging database technologies.

BDAT 610	Introduction to Business Data Analytics	3 Credits
This course serves as an introduction to programming and develops foundational programming skills. The course covers basic principles and practical issues while effectively integrating data analytics topics using Python and R.

Descriptive Analytics

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BDAT 615	Data Analytics II	3 Credits
This course is a follow-up to BDAT-600 and further discusses data analytics techniques as they relate to business decision-making situations. This course uses the R programming language. Prerequisite: BDAT-600

BDAT 620	Data Warehousing	3 Credits
This course focuses on the technology of data warehousing with a specific application towards business intelligence. The course will cover requirements gathering, architecture, and design for data warehousing. Extracting, transforming, and loading strategies to support business intelligence applications will also be covered. Prerequisite: BDAT-600

Predictive Analytics

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BDAT 625	Data Mining	3 Credits
This course examines data mining techniques such as classification, regression, cluster analysis and recommendation systems. Analysis of both structured and unstructured data will be discussed including coverage of NoSQL. Prerequisite: BDAT-600
BDAT 630	Data Visualization	3 Credits
This course discusses techniques for developing effective dashboards to facilitate business decision making. Students will prototype several interactive visualizations based on existing datasets using three industry-leading technologies (Power BI, R Shiny, Bokeh with Python). Prerequisite: BDAT-600

BDAT 640	Forecasting and Predictive Modeling	3 Credits
This covers the concept of predictive analytics, which combines business strategy, information technology, and modeling methods. Prerequisite: BDAT-600

Prescriptive Analytics

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BDAT 635	Advanced Topics in Data Analytics	3 Credits
This course covers advanced timely topics as they related to the field of data analytics. Prerequisite: BDAT-615

BDAT 650	Data Analytics Capstone	3 Credits
This course is the final course of the data analytics program. This course provides students an opportunity to demonstrate the skills learned in the program in a final capstone project experience. Prerequisite: Taken as the last course of the program

Graduate Student Orientation and Conditional Admission Courses

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BUS 500	Graduate Preparation
This course provides all graduate students an opportunity to review essential content prior to starting a graduate program in the John E. Simon School of Business. This course consists of exercises involving paraphrasing, citing, avoiding plagiarism, and business math.
BUS 501	Survey of Business*
This survey course examines the purposes, organization, and major activities of business operations. Emphasis is placed on understanding relationships of business, government, and the consumer in a global economy. This course is designed to give students a brief outline of most of the major activities in business to include accounting and financials, marketing and management.

BUS 541	Business Statistics	3 Credits
This survey course examines the purposes, organization, and major activities of business operations. Emphasis is placed on understanding relationships of business, government, and the consumer in a global economy. This course is designed to give students a brief outline of most of the major activities in business to include accounting and financials, marketing and management.

To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.

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Skills, Concepts, or Opportunities Gained with a Master’s Degree in Data Analytics

A typical master’s in data analytics curriculum consists of courses that can give students in-depth knowledge and skills in several aspects of data analytics. Many of these data analytics courses will cover the following skills, concepts, or opportunities:

Looking for trends, making decisions, and identifying opportunities. More than ever, businesses and organizations are using large amounts of data to make decisions, increase revenue, and find efficiencies; however, all of that data is meaningless without proper analysis. It is critical that students in this field learn how to look for patterns and trends within the data that can signal opportunities or threats and drive decision-making.
Combining operational data with analytical tools. Operational data, which includes data on competitors, suppliers, and finances, can be turned into meaningful information with the right analytical tools. This analysis can, in turn, help improve existing operations.
Presenting complex and competitive information. The amount of data at the fingertips of individuals, organizations, and businesses is staggering. As such, it’s critically important for data analytics professionals to be able to present this information in such a way that other stakeholders — company leadership, for example — can understand it. It’s not enough to just analyze the data; people working in data analytics must also be able to effectively communicate their findings.

Common Courses for MS in Data Analytics Students

These are some of the common courses offered for a data analytics degree. Though actual course titles may vary depending on the university, many data analytics programs offer courses that touch on the following concepts:

Data Analytics. The proper use of data, quantitative analysis, and modeling is driving an increasing number of business decisions. All data analytics students need to be comfortable with analyzing different types of data, using different programming languages, and drawing actionable insights from what they discover.

Database Principles. Much of the data that needs to be analyzed is housed in databases. Becoming familiar with database tools and architecture and relevant security issues is essential for data analytics professionals.

Data Visualization. Looking for and finding meaningful insights in large amounts of data is only half of the job — aspiring data analytics professionals must also be able to visualize the data in a meaningful way in order to inform business decision-making. Common forms of data visualization include charts, graphs, and maps.

Forecasting and Predictive Modeling. The field of predictive analytics is growing quickly within data analytics. Businesses often use forecasting and predictive modeling in order to best predict what may happen in the future.

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